4 papers · 1 filter
The Curse and Blessing of Mean Bias in FP4-Quantized LLM Training
Hengjie Cao, Zhendong Huang, Mengyi Chen +15
FP4 training promises substantial memory and compute savings for large language models, but remains fragile because blockwise quantization is dictated by extreme activation magnitu…
Dispelling the Curse of Singularities in Neural Network Optimizations
Hengjie Cao, Mengyi Chen, Yifeng Yang +11
This work investigates the optimization instability of deep neural networks from a less-explored yet insightful perspective: the emergence and amplification of singularities in the…
Metis: Training LLMs with FP4 Quantization
Hengjie Cao, Mengyi Chen, Yifeng Yang +13
This work identifies anisotropy in the singular value spectra of parameters, activations, and gradients as the fundamental barrier to low-bit training of large language models (LLM…
Selective Learning: Towards Robust Calibration with Dynamic Regularization
Zongbo Han, Yifeng Yang, Changqing Zhang +3
Miscalibration in deep learning refers to there is a discrepancy between the predicted confidence and performance. This problem usually arises due to the overfitting problem, which…